Novel Approaches for Exclusive and Continuous Fingerprint Classification

  • Javier A. Montoya-Zegarra
  • João P. Papa
  • Neucimar J. Leite
  • Ricardo da Silva Torres
  • Alexandre X. Falcão
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)

Abstract

This paper proposes novel exclusive and continuous approaches to guide the search and the retrieval in fingerprint image databases. Both approaches are useful to perform a coarse level classification of fingerprint images before fingerprint authentication tasks. Our approaches are characterized by: (1) texture image descriptors based on pairs of multi-resolution decomposition methods that encode effectively global and local fingerprint information, with similarity measures used for fingerprint matching purposes, and (2) a novel multi-class object recognition method based on the Optimum Path Forest classifier. Experiments were carried out on the standard NIST-4 dataset aiming to study the discriminative and scalability capabilities of our approaches. The high classification rates allow us demonstrate the feasibility and validity of our approaches for characterizing fingerprint images accurately.

Keywords

Feature Vector Optimum Path Minimum Span Tree Query Image Image Descriptor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Javier A. Montoya-Zegarra
    • 1
    • 2
  • João P. Papa
    • 2
  • Neucimar J. Leite
    • 2
  • Ricardo da Silva Torres
    • 2
  • Alexandre X. Falcão
    • 2
  1. 1.Computer Engineering Department, Faculty of EngineeringSan Pablo Catholic UniversityVallecitoPeru
  2. 2.Institute of ComputingState University of CampinasSão PauloBrazil

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